Probabilistic Evaluation of Compound Flooding in Peninsular India: A Copula-Based Analysis of Precipitation, Runoff, and Soil Moisture

Floods are one of India’s most catastrophic natural disasters, causing extensive loss of life and property. Recent research highlights that compound floods—arising from the interplay of multiple drivers—pose greater risks than individual flood events. Although compound flood drivers like precipitation and storm surge, precipitation and runoff, and others have been the focus of recent research globally, very limited research has been done on these flood drivers in India. To address this gap, we conducted a comprehensive compound flood analysis of Peninsular India river basins from 1980 to 2023, utilizing precipitation, runoff, and soil moisture data. Extreme events were identified using a certain percentile threshold (95^th and 99^th percentiles) for all the parameters and each parameter was initially subjected to a univariate analysis. The preliminary results indicate that individual drivers provide limited insights of these flood drivers. To address this, we employed a bivariate copula-based approach to estimate joint distributions at varying percentiles (25th, 50th, 75th, 90th, and 95^th percentile). The analysis using copula was focused to determine of exceedance probability, conditional probability, joint return period, and conditional return period for the paired variables: precipitation-runoff, precipitation-soil moisture, and runoff-soil moisture pairs, respectively. Our results illustrate that, especially in instances where there are multiple contributing components, bivariate analyses provide deeper insights into comprehending the complexity of flood dynamics. Additionally, it has been observed that some regions in our research region had shorter return durations and higher exceedance probabilities, suggesting that compound flood events of lower severity occur frequently. Identical patterns were noted for conditional return durations and conditional probabilities. These results underscore the critical importance of understanding the interconnections among flood drivers for effective flood risk estimation. Our study provides valuable insights for enhancing India’s flood management strategies by identifying disaster-prone regions and informing policymakers in the development of targeted mitigation measures.